Using a DNN to predict the masks that estimate the spatial covariance, which steers the beamformer toward the target signal, is a popular method to combine DNNs and conventional beamforming methods [1]}, [2]}, [3]}. The linear beamformers effectively keep speech free of nonlinear distortion, which is essential for good perceptual quality of speech in communication. However, the linear beamformer cannot cancel all interference, especially those close in space to the speech source. To reduce the residual noise, the beamforming output can be filtered by the mask used for beamforming [1]} or can be processed by a new post enhancement neural network [5]} or even more iterations of neural network and beamforming [6]}. However, adding a new neural network increases the size of the system, which is undesired for its deployment on hardware with limited capacities, such as hearing aids. In this paper, we employ the exact same multi-channel DNN to predict masks for both the estimation of beamformer weights and post enhancement.
